SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 381–390 of 4891 papers

TitleStatusHype
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX—0
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model—0
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification—0
Make Both Ends Meet: A Synergistic Optimization Infrared Small Target Detection with Streamlined Computational Overhead—0
From Precision to Perception: User-Centred Evaluation of Keyword Extraction Algorithms for Internet-Scale Contextual Advertising—0
Data-driven operator learning for energy-efficient building control—0
Kernel Density Machines—0
Generative QoE Modeling: A Lightweight Approach for Telecom Networks—0
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified